LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches-Reference-Cited by-同舟云学术

LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches

Published:2024-05-18 Issue:1 Volume:14 Page:
ISSN:1869-5469
Container-title:Social Network Analysis and Mining
language:en
Short-container-title:Soc. Netw. Anal. Min.

Author:

Bashiri Hadis,Naderi Hassan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13278-024-01268-z.pdf

Reference84 articles.

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3. AminiMotlagh M, Shahhoseini HS, Fatehi N (2023) A reliable sentiment analysis for classification of tweets in social networks. Soc Netw Anal Min 13(1):1–11. https://doi.org/10.1007/s13278-022-00998-2

4. Bahrawi N (2019) Sentiment analysis using random forest algorithm-online social media based. J Inf Technol Util 2(2):29. https://doi.org/10.30818/jitu.2.2.2695

5. Basiri M, Nilchi A, Ghassem-Aghaee N (2014) A framework for sentiment analysis in persian. Open Trans Inf Process 1(3):1–14. https://doi.org/10.15764/otip.2014.03001

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